By Fan Yang, Wenyue Zhao.
With the development of material informatics, it becomes more important to introduce physical information constraints into the ML model. Here, a neural network with physical information constraints (Transformer-based) is designed for the prediction of creep rupture life, which is concerned in alloy design.
05/2/2023 Initial commits:
- Creep data, including creep datasets (.csv).
Note, except alloying elements features (wt.%), the features with "_L12" and "_A1" postfix are contents (at.) of alloying elements in γ'/γ, which calculated by ThermoCalc. - SaTNC model code
- ML model, including SVR, RF, LightGBM, DCSA (refer to https://github.com/wujunming1/mla-shu)
- The processing data: Elemental representation in the stage of Feature Fusion and processing code is provided in "Processing" file
The versions of the pyhton library used are as follows:
pandas -- 1.3.1
numpy -- 1.20.3
scikit-learn -- 1.1.2
lightgbm -- 3.2.1
torch -- 1.9.0